Inversion of satellite ocean colour imagery and geoacoustic characterization of seabed properties: Variational data inversion using a semi-automatic adjoint approach

Badran, Fouad; Berrada, Mohamed; Brajard, Julien; Crépon, Michel; Sorror, Charles; Thiria, Sylvie; Hermand, Jean-Pierre; Meyer, Matthias; Perichon, Laura; Asch, Mark

doi:10.1016/j.jmarsys.2007.02.018

Cited by 13 publications

(17 citation statements)

References 21 publications

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“…YAO has already been tested with success on several models in oceanography. It was applied in the following applications: -Ocean color: variational inversion of multi-spectral satellite ocean color measurements for the restitution of the chlorophyll-a [17,18]. -Marine acoustics: variational inversion of sound speed profile and retrieval of geoacoustic parameters (celerity, density, attenuation, .…”

Section: Yao Presentationmentioning

confidence: 99%

YAO: A Software for Variational Data Assimilation Using Numerical Models

Nardi¹,

Sorror²,

Badran³

et al. 2009

Computational Science and Its Applications – ICCSA 2009

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Abstract. Variational data assimilation consists in estimating control parameters of a numerical model in order to minimize the misfit between the forecast values and some actual observations. The gradient based minimization methods require the multiplication of the transpose jacobian matrix (adjoint model), which is of huge dimension, with the derivative vector of the cost function at the observation points. We present a method based on a modular graph concept and two algorithms to avoid these expensive multiplications. The first step of the method is a propagation algorithm on the graph that allows computing the output of the numerical model and its linear tangent, the second is a backpropagation on the graph that allows the computation of the adjoint model. The YAO software implements these two steps using appropriate algorithms. We present a brief description of YAO functionalities.

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Section: Yao Presentationmentioning

confidence: 99%

YAO: A Software for Variational Data Assimilation Using Numerical Models

Nardi¹,

Sorror²,

Badran³

et al. 2009

Computational Science and Its Applications – ICCSA 2009

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“…as [6], [5]. This YAO application deals with marine acoustics and allows to assimilate some actual observations of acoustic pressure in order to retrieve some geoacoustic parameters like celerity, density and attenuation.…”

Section: Marine Acoustics Examplementioning

confidence: 99%

“…The same operation is done on the modules 1,2,3 and 4, followed again by a level reorganization (M level reduced to 3). The topological order is then: [1,2,3,4], [5], [6] as shown in Fig. 13.…”

Section: Marine Acoustics Examplementioning

confidence: 99%

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YAO: A Generator of Parallel Code for Variational Data Assimilation Applications

Nardi

Badran

Fortin³

et al. 2012

2012 IEEE 14th International Conference on High Performance Computing and Communication &Amp; 2012 IEEE 9th International Confe

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Abstract-Variational data assimilation consists in estimating control parameters of a numerical model in order to minimize the misfit between the forecast values and the actual observations. The YAO framework is a code generator that facilitates, especially for the adjoint model, the writing and the generation of a variational data assimilation program for a given numerical application. In this paper we present how the modular graph specific to YAO enables the automatic and efficient parallelization of the generated code with OpenMP on shared memory architectures. Thanks to this modular graph we are also able to completely avoid the data race conditions (write/write conflicts). Performance tests with actual applications demonstrates good speedups on a multicore CPU.

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“…YAO relies on the theory of graphs [ Berge , 1958] and has the advantage of automatically generating the adjoint of any direct model that has been coded within it. To do so, YAO uses a methodology that is based on the general formalism of a complex system decomposition into modular graphs [ Badran et al , 2008], which is a recurrent procedure in graph theory. YAO then permits to perform data assimilation experiments and is a good platform for their development.…”

Section: Development Of the Variational Assimilationmentioning

confidence: 99%

Improving the parameters of a global ocean biogeochemical model via variational assimilation of in situ data at five time series stations

Kane¹,

Moulin²,

Thiria³

et al. 2011

J. Geophys. Res.

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[1] The global ocean biogeochemical models that are used in order to assess the ocean role in the global carbon cycle and estimate the impact of the climate change on marine ecosystems are getting more and more sophisticated. They now often account for several phytoplankton functional types that play particular roles in marine food webs and the ocean carbon cycle. These phytoplankton functional types have specific physiological characteristics, which are usually poorly known and therefore add uncertainties to model results. Indeed, this evolution in model complexity is not accompanied by a similar increase in the number and diversity of in situ data sets necessary for model calibration and evaluation. Thus, it is of primary importance to develop new methods to improve model performance using existing biogeochemical data sets, despite their current limitations. In this paper, we have optimized 45 physiological parameters of the PISCES global model, using a variational optimal control method. In order to bypass a global 3-D ocean variational assimilation, which would require enormous computation and memory storage, we have simplified the estimation procedure by assimilating monthly climatological in situ observations at five contrasted oceanographic stations of the JGOFS program in a 1-D version of the PISCES model. We began by estimating the weight matrix in the cost function by using heuristic considerations. Then we used this matrix to estimate the 45 parameters of the 1-D version of the PISCES model by assimilating the different monthly profiles (observed profiles at the five stations) in the same variational procedure on a time window of 1 year. This set of optimized parameters was then used in the standard 3-D global PISCES version to perform a 500 year global simulation. The results of both the standard and the optimized versions of the model were compared to satellite-derived chlorophyll-a images, which are an independent and global data set, showing that our approach leads to significant improvements in simulated surface chlorophyll-a in most of the regions of the world ocean. Besides demonstrating that we have improved the accuracy of the PISCES model, this study proposes a sound methodology that could be used to efficiently account for in situ data in biogeochemical ocean models.

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Inversion of satellite ocean colour imagery and geoacoustic characterization of seabed properties: Variational data inversion using a semi-automatic adjoint approach

Cited by 13 publications

References 21 publications

YAO: A Software for Variational Data Assimilation Using Numerical Models

YAO: A Software for Variational Data Assimilation Using Numerical Models

YAO: A Generator of Parallel Code for Variational Data Assimilation Applications

Improving the parameters of a global ocean biogeochemical model via variational assimilation of in situ data at five time series stations

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